GIS-fuzzy logic technique in modeling soil depth classes: Using parts of the Clay Belt and Hornepayne region in Ontario, Canada as a case study

Akumu, Clement E.; Woods, Murray; Johnson, Jesse W.; Pitt, Doug; Uhlig, P.; McMurray, S.

doi:10.1016/j.geoderma.2016.07.028

Cited by 9 publications

(5 citation statements)

References 110 publications

(133 reference statements)

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“…If a variable belongs to a set, the model would take a value between 0 to 1 instead of 0 or 1. Several studies have used the DTM-based FL to improve soil taxonomic classes in soil mapping [97][98][99][100][101], soil texture and soil horizonation prediction [98,[102][103][104][105], and soil vulnerability classification [106]. Qi et al [102] found that using the FL the accuracy of soil series name prediction increased 17% compared to the conventional soil survey.…”

Section: Dtm-based Methods To Predict Categorical Soil Propertiesmentioning

confidence: 99%

Application of Topographic Analyses for Mapping Spatial Patterns of Soil Properties

Li¹,

McCarty²

2019

Earth Observation and Geospatial Analyses [Working Title]

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Landscape topography is a key parameter impacting soil properties on the earth surface. Strong topographic controls on soil morphological, chemical, and physical properties have been reported. This chapter addressed applications of topographical information for mapping spatial patterns of soil properties in recent years. Objectives of this chapter are to provide an overview of (1) impacts of topographic heterogeneity on the spatial variability in soil properties and (2) commonly used topography-based models in soil science. A case study was provided to demonstrate the feasibility of applying topography-based models developed in field sites to predict soil property over a watershed scale. A large-scale soil property map can be obtained based on topographic information derived from high-resolution remotely sensed data, which would benefit studies in areas with limited data accesses or needed to extrapolate findings from representative sites to larger regions.

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Section: Dtm-based Methods To Predict Categorical Soil Propertiesmentioning

confidence: 99%

Application of Topographic Analyses for Mapping Spatial Patterns of Soil Properties

Li¹,

McCarty²

2019

Earth Observation and Geospatial Analyses [Working Title]

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“…For example, Lopes et al (2011) used PCA and PCR analysis together to better understand which variables were most important in the formation of beach cusps along coastlines. However, by taking the knowledge gained through PCA and PCR analysis, our study was able to incorporate fuzzy logic predictability mapping (Akumu et al, 2016; Caniani et al, 2016) to assess the effects of regeneration predictor variables on aspen regeneration across the harvested block. By incorporating fuzzy logic into our model, it allowed for the creation of regeneration suitability levels, which were used to delineate the harvested blocks, and better understand the differences in the regeneration predictor variables between the suitability levels.…”

Section: Discussionmentioning

confidence: 99%

“…Lastly, aspen leaves were oven-dried at 80°C for 48 h and then weighed to obtain a measure of total oven-dry leaf biomass (g m -2 ). Figure 1 is an illustration of the new fuzzy logic cumulative effects assessment method, which used PCA and PCR analysis (Lopes et al, 2011) along with fuzzy logic suitability mapping (Akumu et al, 2016;Caniani et al, 2016) to calculate aspen regeneration suitability. This figure depicts in sequence the ArcGIS tools and processes that were used in this method to determine a harvested block's suitability for aspen regeneration.…”

Section: Aspen Regeneration Indicatorsmentioning

confidence: 99%

Quantifying Cumulative Effects of Harvesting on Aspen Regeneration through Fuzzy Logic Suitability Mapping

Sealey

Amichev

Rees

2019

Soil Science Soc of Amer J

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Core Ideas Multivariate statistics and fuzzy logic analysis were used to assess aspen regeneration. Vegetation indices were the dominant factors in determining regeneration suitability. Vegetation indices were significantly higher in high suitability areas. Skidder traffic was significantly lower on high regeneration suitability land. The percentage of slash coverage was significantly lower on high suitability land. Vigorous aspen (Populus tremuloides Michx.) regeneration immediately following a harvesting event is important to ensuring the continued health and productivity of the future forest. This study aimed to examine the potential of using unoccupied aerial vehicle, multispectral remote sensing, and GIS mapping techniques to develop a comprehensive approach for predicting aspen regeneration success at the harvest block scale. Three winter harvested blocks were studied at Duck Mountain Provincial Park in east‐central Saskatchewan, Canada. Ten regeneration predictor variables (number of skidder passes, percentage slash coverage, topographic wetness index, slope, aspect, slope position, and four vegetation indices: green normalized vegetation index [GNDVI], normalized red‐edge index [NDRE], simple RED to NIR ratio [SR], and chlorophyll index green [CIG]) were determined for 168 measurement plots 1 yr after harvest. Principal component analysis, principal component regression, fuzzy logic analysis, and GIS mapping techniques, were combined for the first time in this study to determine cumulative effects on aspen regeneration. On average, low suitability areas had significantly more skidder traffic (34 passes) compared to below average (17), above average (10), and high (7) suitability areas. Low suitability areas also had significantly more slash coverage (13.1%) compared to below average (8.49%) or high suitability land (7.18%). High suitability areas had significantly higher GNDVI, NDRE, SR, and CIG indices, compared to low and below average suitability land. Not only does this method of analysis help to assess how a combination of factors may influence aspen regeneration, it can also act as a decision support system tool for industry, or government, to improve aspen regeneration assessments.

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“…Topography-based FL has been applied in soil mapping to improve soil taxonomic classification [63][64][65][66][67]. Several studies also used the model to study spatial patterns of soil horizonation [64,68,69], predict soil texture [68,70], and classify soil vulnerability [71].…”

Section: Logic Modelsmentioning

confidence: 99%

Use of Topographic Models for Mapping Soil Properties and Processes

Li¹,

McCarty²,

Du³

et al. 2020

Soil Syst.

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Landscape topography is an important driver of landscape distributions of soil properties and processes due to its impacts on gravity-driven overland and intrasoil lateral transport of water and nutrients. Rapid advancements in aerial, space, and geographic technologies have led to large scale availability of digital elevation models (DEMs), which have proven beneficial in a wide range of applications by providing detailed topographic information. In this report, we presented a summary of recent topography-based soil studies and reviewed five main groups of topographic models in geospatial analyses widely used for soil sciences. We then compared performances of two types of topography-based models—topographic principal component regression (TPCR) and TPCR-kriging (TPCR-Kr)—to ordinary kriging (OKr) models in mapping spatial patterns of soil organic carbon (SOC) density and redistribution (SR) rate. The TPCR and OKr models were calibrated at an agricultural field site that has been intensively sampled, and the TPCR and TPCR-Kr models were evaluated at another field of interest with two sampling transects. High-resolution topographic variables generated from light detection and ranging (LiDAR)-derived DEMs were used as inputs for the TPCR model building. Both TPCR and OKr models provided satisfactory results on SOC density and SR rate estimations during model calibration. The TPCR models successfully extrapolated soil parameters outside of the area in which the model was developed but tended to underestimate the range of observations. The TPCR-Kr models increased the accuracies of estimations due to the inclusion of residual kriging calculated from observations of transects for local correction. The results suggest that even with low sample intensives, the TPCR-Kr models can reduce estimation variances and provide higher accuracy than the TPCR models. The case study demonstrated the feasibility of using a combination of linear regression and spatial correlation analysis to localize a topographic model and to improve the accuracy of soil property predictions in different regions.

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GIS-fuzzy logic technique in modeling soil depth classes: Using parts of the Clay Belt and Hornepayne region in Ontario, Canada as a case study

Cited by 9 publications

References 110 publications

Application of Topographic Analyses for Mapping Spatial Patterns of Soil Properties

Application of Topographic Analyses for Mapping Spatial Patterns of Soil Properties

Quantifying Cumulative Effects of Harvesting on Aspen Regeneration through Fuzzy Logic Suitability Mapping

Use of Topographic Models for Mapping Soil Properties and Processes

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